Key Factors for the Success of Self-Administered Treatments of Poststroke Aphasia Using Technologies
Bibliographic record
Abstract
Background: Use of technology in language rehabilitation has grown significantly in recent years, and there is increasing evidence of its effectiveness in the treatment of poststroke aphasia. Technology has the potential to foster intensity and repetition by enabling people with aphasia to improve their skills without the constant presence of the clinician. The main objective of this article is to review and illustrate key factors for the success of self-administered treatments of poststroke aphasia using technologies. Methods: We briefly reviewed technology-based treatments of aphasia and described three determining factors for the success of self-administered treatments delivered by technology, namely, treatment-related, technology-related, and patient-related factors. Two clinical cases were also presented to illustrate issues and challenges related to the various factors to be considered before proposing such treatments. Conclusions: Self-administered treatments of poststroke aphasia using new technologies enable patients to be more independent in their rehabilitation and to benefit from more intensive and extended treatment. These benefits are important in the current economic context, where human and financial resources for clinical practice are limited. Speech-language therapists should consider these opportunities and propose new methods to deliver attractive and intensive treatments of poststroke aphasia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".